def sem(d):
try:
sn = SenticNet()
sn.semantics(d)
return True
except KeyError:
return False
def fun1(d):
try:
from senticnet.senticnet import SenticNet
sn = SenticNet()
sn.semantics(d)
return True
except KeyError as error:
return False
from senticnet.senticnet import SenticNet
sn = SenticNet()
print("polarity value:", sn.polarity_value("love"))
print("polarity intense:", sn.polarity_intense("love"))
print("moodtags:", ", ".join(sn.moodtags("love")))
print("semantics:", ", ".join(sn.semantics("love")))
print("\n".join([key + ": " + str(value) for key, value in sn.sentics("love").items()]))
# Each line of corpus must be equivalent to each document of the corpus
#boc_model=boc.BOCModel(doc_path="input corpus path")
boc_model = boc.BOCModel('text.txt')
#boc_model.context = text
# output can be saved with save_path parameter
boc_matrix, word2concept_list, idx2word_converter = boc_model.fit()
# SenitcNet lexicon lookup
from senticnet.senticnet import SenticNet
sn = SenticNet()
concept_info = sn.concept(text)
polarity_value = sn.polarity_value(text)
polarity_intense = sn.polarity_intense(text)
moodtags = sn.moodtags(text)
semantics = sn.semantics(text)
sentics = sn.sentics(text)
print('==================================')
print('test: ', text)
print('concept_info: ', concept_info)
print('polarity_value: ', polarity_value)
print('polarity_intense: ', polarity_intense)
print('moodtags: ', moodtags)
print('semantics: ', semantics)
print('sentics: ', sentics)
print('==================================')
from senticnet.senticnet import SenticNet
teste = []
sn = SenticNet('pt')
concept_info = sn.concept('amor')
polarity_value = sn.polarity_value('amor')
polarity_intense = sn.polarity_intense('amor')
moodtags = sn.moodtags('amor')
semantics = sn.semantics('amor')
sentics = sn.sentics('amor')
teste.append(concept_info)
print(teste)
